Estimation of Surface Soil Moisture in Cornfields Using a Modified MODIS-Based Index and Considering Corn Growth Stages

Abstract

In this paper, we propose a new method for estimating soil moisture in cornfields, based on regression models using moderate resolution imaging spectroradiometer (MODIS) data and field measurements. Soil moisture is related to the perpendicular drought index (PDI) and vegetation supply water index (VSWI). Thus, we propose a modified MODIS-based index (MMI) combining PDI and VSWI, to build regression models incorporating MMI and in situ soil moisture measurements. In the MMI, PDI is used to estimate soil moisture in areas with bare soil or sparse vegetation cover, and VSWI is used for estimation of soil moisture in areas with other cover types. Considering the dependence of soil moisture estimation on corn growth stages, we incorporated five growth stages—seeding, seedling, growing, maturing, and harvesting stages in the regression models for soil moisture estimation. These models were built for each stage using five years of soil moisture data obtained through field measurements combined with daily MMI at 1-km spatial resolution. Our results show that the MMI is strongly correlated to in situ soil moisture at each corn growth stage, and furthermore, these correlations are dependent on vegetation cover and growth stage. The correlation coefficients between field-measured and model-estimated soil moisture were 0.73, 0.67, 0.66, 0.74, and 0.63 for each growth stage, respectively. The estimated soil moisture was more accurate than SMOS-BEC soil moisture Level 3 products. These findings show that our model is a feasible method for estimating surface soil moisture accurately at the 1-km scale for cornfields in the study area.